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Gao, F., & Oldengott, I. M. (2022). Cosmology Meets Functional QCD: First-Order Cosmic QCD Transition Induced by Large Lepton Asymmetries. Phys. Rev. Lett., 128(13), 131301–6pp.
Abstract: The lepton flavor asymmetries of the Universe are observationally almost unconstrained before the onset of neutrino oscillations. We calculate the cosmic trajectory during the cosmic QCD epoch in the presence of large lepton flavor asymmetries. By including QCD thermodynamic quantities derived from functional QCD methods in our calculation, our work reveals for the first time the possibility of a first-order cosmic QCD transition. We specify the required values of the lepton flavor asymmetries for which a first-order transition occurs for a number of different benchmark scenarios.
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Garani, R., & Palomares-Ruiz, S. (2022). Evaporation of dark matter from celestial bodies. J. Cosmol. Astropart. Phys., 05(5), 042–53pp.
Abstract: Scatterings of galactic dark matter (DM) particles with the constituents of celestial bodies could result in their accumulation within these objects. Nevertheless, the finite temperature of the medium sets a minimum mass, the evaporation mass, that DM particles must have in order to remain trapped. DM particles below this mass are very likely to scatter to speeds higher than the escape velocity, so they would be kicked out of the capturing object and escape. Here, we compute the DM evaporation mass for all spherical celestial bodies in hydrostatic equilibrium, spanning the mass range [10(-)(10) – 10(2)] M-circle dot, for constant scattering cross sections and s-wave annihilations. We illustrate the critical importance of the exponential tail of the evaporation rate, which has not always been appreciated in recent literature, and obtain a robust result: for the geometric value of the scattering cross section and for interactions with nucleons, at the local galactic position, the DM evaporation mass for all spherical celestial bodies in hydrostatic equilibrium is approximately given by E-c/T-chi similar to 30, where E-c is the escape energy of DM particles at the core of the object and T-chi is their temperature. In that case, the minimum value of the DM evaporation mass is obtained for super-Jupiters and brown dwarfs, m(ev)(ap) similar or equal to 0.7 GeV. For other values of the scattering cross section, the DM evaporation mass only varies by a factor smaller than three within the range 10(-41) cm(2) <= sigma(p) <= 10(-31) cm(2), where sigma(p) is the spin-independent DM-nucleon scattering cross section. Its dependence on parameters such as the galactic DM density and velocity, or the scattering and annihilation cross sections is only logarithmic, and details on the density and temperature profiles of celestial bodies have also a small impact.
Keywords: dark matter detectors; dark matter theory; massive stars; stars
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Garcia Navarro, J. E., Fernandez-Prieto, L. M., Villaseñor, A., Sanz, V., Ammirati, J. B., Diaz Suarez, E. A., et al. (2022). Performance of Deep Learning Pickers in Routine Network Processing Applications. Seismol. Res. Lett., 93, 2529–2542.
Abstract: Picking arrival times of P and S phases is a fundamental and time‐consuming task for the routine processing of seismic data acquired by permanent and temporary networks. A large number of automatic pickers have been developed, but to perform well they often require the tuning of multiple parameters to adapt them to each dataset. Despite the great advance in techniques, some problems remain, such as the difficulty to accurately pick S waves and earthquake recordings with a low signal‐to‐noise ratio. Recently, phase pickers based on deep learning (DL) have shown great potential for event identification and arrival‐time picking. However, the general adoption of these methods for the routine processing of monitoring networks has been held back by factors such as the availability of well‐documented software, computational resources, and a gap in knowledge of these methods. In this study, we evaluate recent available DL pickers for earthquake data, comparing the performance of several neural network architectures. We test the selected pickers using three datasets with different characteristics. We found that the analyzed DL pickers (generalized phase detection, PhaseNet, and EQTransformer) perform well in the three tested cases. They are very efficient at ignoring large‐amplitude transient noise and at picking S waves, a task that is often difficult even for experienced analysts. Nevertheless, the performance of the analyzed DL pickers varies widely in terms of sensitivity and false discovery rate, with some pickers missing a significant percentage of true picks and others producing a large number of false positives. There are also variations in run time between DL pickers, with some of them requiring significant resources to process large datasets. In spite of these drawbacks, we show that DL pickers can be used efficiently to process large seismic datasets and obtain results comparable or better than current standard procedures.
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Garcia Soto, A., Zhelnin, P., Safa, I., & Arguelles, C. A. (2022). Tau Appearance from High-Energy Neutrino Interactions. Phys. Rev. Lett., 128(17), 171101–7pp.
Abstract: High-energy muon and electron neutrinos yield a non-negligible flux of tau neutrinos as they propagate through Earth. In this Letter, we address the impact of this additional component in the PeV and EeV energy regimes for the first time. Above 300 TeV, this contribution is predicted to be significantly larger than the atmospheric background, and it alters current and future neutrino telescopes' capabilities to discover a cosmic tau-neutrino flux. Further, we demonstrate that Earth-skimming neutrino experiments, designed to observe tau neutrinos, will be sensitive to cosmogenic neutrinos even in extreme scenarios without a primary tau-neutrino component.
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Gariazzo, S., de Salas, P. F., Pisanti, O., & Consiglio, R. (2022). PArthENoPE revolutions. Comput. Phys. Commun., 271, 108205–13pp.
Abstract: This paper presents the main features of a new and updated version of the program PArthENoPE, which the community has been using for many years for computing the abundances of light elements produced during Big Bang Nucleosynthesis. This is the third release of the PArthENoPE code, after the 2008 and the 2018 ones, and will be distributed from the code's website, http://parthenope.na.infn.it. Apart from minor changes, the main improvements in this new version include a revisited implementation of the nuclear rates for the most important reactions of deuterium destruction, H-2(p,gamma) He-3, H-2(d, n)He-3 and H-2(d, p)H-3, and a re-designed GUI, which extends the functionality of the previous one. The new GUI, in particular, supersedes the previous tools for running over grids of parameters with a better management of parallel runs, and it offers a brand-new set of functions for plotting the results. Program summary Program title: PArthENoPE 3.0 CPC Library link to program files: https://doi.org/10.17632/wygr7d8yt9.2 Developer's repository link: http://parthenope.na.infn.it Licensing provisions: GPLv3 Programming language: Fortran 77 and Python Nature of problem: Computation of yields of light elements synthesized in the primordial universe Solution method: Livermore Solver for Ordinary Differential Equations (LSODE) for stiff and nonstiff systems, Python GUI for running and plotting Journal reference of previous version: Comput. Phys. Commun. 233 (2018) 237-242 Does the new version supersede the previous version?: Yes Reasons for the new version: Update of the physics and improvements in the GUI Summary of revisions: Update of the physics implemented in the Fortran code and improvements in the GUI functionalities, in particular new plotting functions.
Keywords: Primordial nucleosynthesis; Cosmology; Neutrino physics
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